All models were implemented within the Pytorch deep learning framework with a Resnet50 model used for the backbone architecture and a ResNet18 used for the hand model. All models utilised Kinetics-400[1]} pretraining, with batch normalisation statistics frozen from pretrained weights to reduce overfitting. The initial learning rate was set to 0.002 for parameters within the backbone model and 0.0002 for parameters within the hand feature extraction model, with gradient clipping set to 20. Parameter weight decay was set to 0.0005 and momentum set to 0.9 for all parameters. All models were trained for 150 epochs with a batch size of 64 (except for the 16 frame model which utilised a batch size of 32), with learning rate decreased by a factor of 10 at 100 and 125 epochs. A dropout of 0.8 was implemented in the final fully connected layer of all model to reduce overfitting.
